Author

Aldwin Arambulo (SID 510575452) | Mia Childers (SID 510414892) | Mu-Wei Chung (SID 510611635)

Published

July 24, 2024

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This report investigates the impacts of vaccinations amongst the different development regions as we dived into the difference of life expectancy and the global coverage of the vaccination programs. We used a dataset from OWID (Our World In Data) for vaccination coverage and global life expectancy. We analysed the trend of the vaccination programs in different regions to deduce the impact of the vaccines and potential life expectancy gains.

Life Expectancy Data

Life Expectancy dataset from Our World in Data.

Code
le_df <- read_csv("data/life-expectancy.csv")
paged_table(le_df)

Cleaning life expectancy data

Code
le_clean <- janitor::clean_names(le_df)
# rename column
le_clean <- le_clean %>% 
  rename(period_life_expect = period_life_expectancy_at_birth_sex_all_age_0)
paged_table(le_clean)

Global Vaccination Coverage Data

Code
vaccination_df <- read_csv("data/global-vaccination-coverage.csv")
vaccination_df %>% paged_table()

Cleaning vaccination data

Code
vaccination_clean <- janitor::clean_names(vaccination_df) %>% paged_table()
vaccination_clean

Using a combination of Pearson’s correlation coefficient and linear regression, the vaccines for one dose of Salk’s polio vaccine, one dose of the meningococcal vaccine, three doses of the polio vaccine and three doses of the DPT (diphtheria, pertussis, tetanus) vaccine were found to be the most significant in increasing life expectancy globally.

Code
merged_datasets = merge(vaccination_clean, le_clean, by=c("entity","year"))
names(merged_datasets)[names(merged_datasets) == 'period_life_expectancy_at_birth_sex_all_age_0'] <- 'life_expectancy'

References